As medical imaging technologies advance, more and more image data sets are acquired by medical scanners to facilitate the diagnosis. For a given patient, based on information collected about the patient, the clinician has the task of establishing a diagnosis of the patient. To this end, the clinician may have a hypothetical diagnosis he wishes to confirm or reject, based on results of patient examination, such as medical images or laboratory results. In the case of medical images, the clinician maps the hypothesis into image findings which can help in confirming or rejecting the diagnosis. To obtain the image findings, the clinician not only needs to acquire the necessary medical images, using an imaging modality such as x-ray, CT, or MR, but he also needs to generate appropriate views of the acquired medical image data. Such views may be generated using an image processing system. Such view generation may include manually setting appropriate contrast and brightness levels, zoom levels, and panning, for example. Three-dimensional image data may need to be processed to create, for example, a slice view, multi-planar reformat view, or a perspective view from a particular perspective. In this process, the manual generation of views can be very time-consuming. For example, to diagnose a particular type of cancer, the necessary image findings may include vessel enhancement in a specific region of an organ. To be able to obtain such image findings from a 3D medical image, the clinician would have to create appropriate cross-sectional views or 3D rendered views showing the particular vessels within the organ. These steps may be time-consuming.